| Mobile traffic prediction from raw data using LSTM networks HD Trinh, L Giupponi, P Dini 2018 IEEE 29th annual international symposium on personal, indoor and mobile …, 2018 | 212 | 2018 |
| Detecting Mobile Traffic Anomalies through Physical Control Channel Fingerprinting: a Deep Semi-supervised Approach HD Trinh, E Zeydan, L Giupponi, P Dini IEEE Access 7 (1), 152187-152201, 2019 | 70 | 2019 |
| Analysis and modeling of mobile traffic using real traces HD Trinh, N Bui, J Widmer, L Giupponi, P Dini 2017 IEEE 28th annual international symposium on personal, indoor, and …, 2017 | 54 | 2017 |
| Urban Anomaly Detection by processing Mobile Traffic Traces with LSTM Neural Networks HD Trinh, L Giupponi, P Dini 2019 IEEE International Conference on Sensing, Communication and Networking …, 2019 | 47 | 2019 |
| Mobile Traffic Classification through Physical Control Channel Fingerprinting: a Deep Learning Approach HD Trinh, AF Gambin, L Giupponi, M Rossi, P Dini IEEE Transactions on Network and Service Management, 2020 | 34 | 2020 |
| Wake-up scheduling for energy-efficient mobile devices S Rostami, HD Trinh, S Lagen, M Costa, M Valkama, P Dini IEEE Transactions on Wireless Communications 19 (9), 6020-6036, 2020 | 16 | 2020 |
| Mobile Traffic Classification through Physical Control Channel Fingerprinting: a Deep Learning Approach HD Trinh, AG Fernandez, L Giupponi, M Rossi, P Dini arXiv, arXiv: 1910.11617, 2019 | 11* | 2019 |
| Proactive wake-up scheduler based on recurrent neural networks S Rostami, HD Trinh, S Lagen, M Costa, M Valkama, P Dini ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-6, 2020 | 6 | 2020 |
| Engin Zeydan, and Lorenza Giupponi,“Detecting mobile traffic anomalies through physical control channel fingerprinting: A deep semi-supervised approach,” HD Trinh IEEE Access 7, 152187-152201, 2019 | 6 | 2019 |
| Unveiling Radio Resource Utilization Dynamics of Mobile Traffic through Unsupervised Learning A Rago, G Piro, HD Trinh, G Boggia, P Dini TMA 2 (4), 2019 | 6 | 2019 |
| Data analytics for mobile traffic in 5G networks using machine learning techniques HD Trinh Universitat Politècnica de Catalunya, 2020 | 5 | 2020 |
| Engin Zeydan, L. Giupponi, and Paolo Dini.«Detecting Mobile Traffic Anomalies through Physical Control Channel Fingerprinting: a Deep Semi-supervised Approach» HD Trinh IEEE Access, 1-1, 0 | 2 | |
Paolo DiniCTTC/CERCA | Sustainable AI research unitAdresse e-mail validée de cttc.es